Impact load identification of composite structure using genetic algorithms

For structural health monitoring of composite structure, it is important to quickly and accurately identify the impact load whenever an impact event occurs. This paper proposes a genetic algorithms (GA)-based approach for impact load identification, which can identify the impact location and reconstruct the impact force history simultaneously. In this study, impact load is represented by a set of parameters, thus the impact load identification problem in both space (impact location) and time (impact force history) domains is transformed to a parameter identification problem. A forward model characterizes the dynamic response of the structure subject to a known impact force is incorporated in the identification procedure. By minimizing the difference between the analytical responses given by the forward model and the measured ones, GA adaptively identify the impact location and force history with its global search capability. This new impact identification approach is applied to a stiffened composite panel. The stiffened composite panel is modeled as an equivalent laminate with varying properties and the forward response is obtained by using an assumed modes approach. To improve the computational efficiency, micro-GA (μGA) is employed to perform the identification task. Numerical simulation studies are conducted to demonstrate the effectiveness and applicability of the proposed method.